摘要
异构数据在各种实际应用中大量存在,异构数据的建模与关联分析占有重要地位,传统的概念格模型以及现有的各种扩展模型已经不适应这种需求.本文对Ganter和Wille定义的Galois连接函数f(A)和g(B)进行修正,讨论了异构数据集上的偏序形成,提出了面向异构数据分析的广义概念格模型.文中事件分析的例子表明:采用广义概念格模型可以对异构数据建模型,进行关联分析挖掘隐含知识.
Heterogeneous data permeate in nearly every application and research field .Modeling and correlation analysis about heterogeneous data is the bottleneck of the conventional concept lattice as well as its various type of extended model .This pa-per presents a generalized concept lattice model by altering the definition of Galois connection function f （ A ） and g （ B ） given by Ganter and Wille ,and discusses the ordering on heterogeneous attributes .After a survey of event-oriented knowledge representation , an event model formalized by generalized concept lattice is presented as an application ,which is capable of dealing with different types of attributes and mining useful knowledge .This example gives a clue of generalized concept lattice model application in other fields .
出处
《电子学报》
EI
CAS
CSCD
北大核心
2013年第12期2451-2455,共5页
Acta Electronica Sinica
基金
国家自然科学基金(No.60975033)
河南理工大学博士基金(No.B2011-102)
关键词
形式概念分析
概念格
异构数据
事件建模
formal concept analysis
concept lattice
heterogeneous data
event modeling
